TILDE is a D&C approach.
TILDE behaves similarly to the decision tree learning algorithm C4.5 [1]}.
To find a hypothesis TILDE employs a D&C strategy by recursively dividing the examples into disjoint subsets.
TILDE differs from C4.5 by how it generates candidate splits to partition the examples.
C4.5 generates candidates as attribute-value pairs.
By contrast, TILDE uses conjunctions of literals (i.e. clauses/rules).
TILDE can learn large programs and can scale to large datasets.
However, it has several limitations, notably an inability to learn recursive programs, no predicate invention, and difficulty learning from small numbers of examples.
